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Comprehensive developmental somatic proteome atlas of Haemonchus contortus underpinned by a chromosome-scale genome and deep tandem mass spectrometry
No full textBackground: Haemonchus contortus is a highly pathogenic, blood-feeding nematode that causes widespread disease and significant economic loss in livestock worldwide. Previous proteomic studies were constrained by incomplete genomic resources and limited analytical sensitivity, impeding comprehensive profiling across life stages. Methods: In this study, we integrated advanced tandem mass spectrometry with a chromosome-scale genome assembly of the Haecon-5 strain to construct the most detailed somatic proteome of H. contortus to date. Results: We identified and quantified 7002 proteins across five key developmental stages/sexes—i.e. eggs, third-stage larvae (L3s), fourth-stage larvae (L4s), and adult female (Af) and adult male (Am) worms—tripling the number identified in an earlier study. Comparative analyses revealed pronounced stage-specific expression and functional specialisation, with parasitic stages enriched in proteins linked to metabolism, cellular function and environmental sensing. Fifteen proteins associated with the hypoxia-inducible factor 1 (HIF-1) signalling pathway were upregulated in parasitic stages, suggesting a role in adaptation to hypoxia. Additionally, 150 proteases implicated in haemoglobin degradation were characterised, and functional assays confirmed markedly elevated haemoglobinolytic activity in blood-feeding stages. Conclusions: These findings offer key insights into H. contortus development and parasitism, and establish a high-resolution proteomic framework to underpin fundamental biological studies and to enable the discovery of molecular targets for novel interventions against this and related nematodes
The role of autoantibodies in Alzheimer's disease: Pathogenetic connections or epiphenomena?
No full textINTRODUCTION: The current evidence supporting the complex, multifaceted etiology for Alzheimer's disease (AD) grows by the day, prompting increased research in non-"amyloid hypothesis"-related pathways. One of these pathways of interest pertains to an autoimmune component in this disease. METHODS: In this review, we briefly discuss current evidence of potential contributions of autoimmunity to AD pathobiology and describe the putative role of autoantibodies detected in patient fluids. We draw attention to the fact that the reported AD-related autoantibodies differ dramatically between published studies, raising doubts about the reliability and robustness of these findings. RESULTS: We hypothesize, and provide indirect evidence, that many of the reported autoantibodies in AD may represent false discoveries. We suggest follow-up validation and confirmatory studies with sufficient power, preferably by employing orthogonal testing techniques. DISCUSSION: Uncovering the putative autoimmune components of AD is important and could pave the way to new concepts for AD pathogenesis, diagnosis, and therapy. HIGHLIGHTS: Although Alzheimer's disease (AD) is not traditionally considered an autoimmune disease, growing evidence suggests immune system dysregulation and autoantibody generation, either in the form of naturally occurring or pathogenic autoantibodies. Numerous studies have discovered autoantibodies in AD, but only a few of them have been found independently and multiple times, including amyloid β (Aβ) and tau autoantibodies. Many of these findings represent false discoveries. Follow-up validation and confirmatory studies with sufficient power are imperative, preferably by employing orthogonal testing techniques. Understanding the immune and autoimmune landscape in AD will assist in future immunotherapy strategies
Integrating lean and resilience: a healthcare supply chain perspective
No full textPurpose
This research aims to analyze the deployment of lean practices and resilience capabilities within the healthcare supply chain across different disruptive scenarios. The study addresses the gap in how different tier levels of the healthcare supply chain integrate lean and resilience.
Design/methodology/approach
Employing a case study approach, the research evaluated four Italian organizations (two healthcare providers, one pharmaceutical distributor and one pharmaceutical producer) representing the three main tier levels of the healthcare supply chain. The methodology involved a questionnaire assessing the adoption of specific lean practices and resilience capabilities, followed by a scenario analysis by experts used to identify critical practices and capabilities across different disruptive scenarios.
Findings
This research systematically identified critical lean practices and resilience capabilities that are underutilized at various tier levels within the healthcare supply chain, highlighting significant opportunities for theoretical advancement in operational efficiency and system robustness during disruptions. Additionally, the study introduced a novel methodological approach to evaluate the effectiveness of lean and resilience practices across different disruptive scenarios, thereby enriching the theoretical framework for crisis management within healthcare operations. Finally, we emphasized the crucial roles of just-in-time and anticipation capability in bolstering the performance of all the healthcare supply chain.
Originality/value
The study contributes to the fields of supply chain management and healthcare by systematically identifying and classifying the importance of lean practices and resilience capabilities in managing disruptions. Additionally, the potential for cross-tier collaboration and knowledge sharing to enhance overall supply chain resilience is highlighted
Chronic behavioral and seizure outcomes following experimental traumatic brain injury and comorbid Klebsiella pneumoniae lung infection in mice
No full textOBJECTIVE: Traumatic brain injury (TBI) is a leading cause of long-term disability, and infections such as pneumonia represent a common and serious complication for patients with TBI in the acute and subacute post-injury period. Although the acute effects of infections have been documented, their long-term consequences on neurological and behavioral recovery as well as the potential precipitation of seizures after TBI remain unclear. This study aimed to investigate the chronic effects of Klebsiella pneumoniae infection following TBI, focusing on post-traumatic seizure development and neurobehavioral changes. METHODS: Using a mouse model, we assessed the long-term effects of TBI and K. pneumoniae infection both in isolation and in combination. RESULTS: We found that, although infection with K. pneumoniae resulted in loss of body weight and increased mortality compared to vehicle-inoculated mice, there was no additional mortality in TBI animals. Furthermore, although TBI alone induced chronic hyperactivity and reduced anxiety-like behaviors, K. pneumoniae lung infection had no lasting effect on these long-term outcomes. Third, although TBI resulted in both spontaneous and evoked seizures long-term post-injury, early post-injury K. pneumoniae infection did not affect late-onset seizure susceptibility. SIGNIFICANCE: Together with recent findings on acute outcomes in this combined insult model of TBI and K. pneumoniae infection, this study suggests that K. pneumoniae does not significantly alter long-term neurobehavioral outcomes or the development of post-traumatic epilepsy. This research highlights the need to further explore the interplay between additional immune insults such as infection that may influence long-term recovery
The Role of Foxes in Transmitting Zoonotic Bacteria to Humans: A Scoping Review
No full textZoonotic diseases inflict substantial burdens on human and animal populations worldwide, and many of these infections are bacterial. An Australian study investigating environmental risk factors for Buruli ulcer in humans detected the causative agent, Mycobacterium ulcerans , in the faeces of wild foxes, a novel finding that suggests foxes may be implicated in the transmission of this zoonotic bacterium. The aim of this scoping review was to systematically search and examine the global data for reports implicating foxes in the transmission of zoonotic bacteria to humans. A pre-tested search strategy was implemented in five bibliographic databases (PubMed, Embase, CAB Abstracts, Cochrane Trials, Google Scholar). Eligible studies presented primary research data about zoonotic bacterial diseases that were confirmed or presumed to have been transmitted via foxes (excluding exclusively blood- or vector-borne bacteria), with no restrictions on geographical setting or publication year. The final dataset included ten primary research articles, with varying study designs, settings, populations and testing methods. The described bacterial zoonoses were anthrax, cutaneous diphtheria, leptospirosis, faecal coliforms including E. coli , tularaemia, yersiniosis, and Buruli ulcer (the study that was the impetus for this scoping review). Fox-human bacterial transmission was confirmed in one human case and considered likely to have occurred in certain high-risk groups in another. The likelihood of fox-human transmission having occurred in the remaining studies was possible (n = 5) or unlikely (n = 3). Identified and hypothesised drivers of fox-human transmission included accidental and occupational factors. Published reports of fox-human transmission of zoonotic bacteria are few, and generally indicative of relatively low risk. However, foxes can transmit zoonotic pathogens including bacteria to humans in a variety of settings, and human-fox encounters are likely to increase with ongoing anthropogenic activities. Further research and public education campaigns would help increase knowledge and awareness of fox-associated zoonoses
Training Schedule Affects Operant Responding Independent of Motivation in the Neuroligin-3 R451C Mouse Model of Autism
No full textAutism affects ~1 in 100 people and arises from the interplay between rare genetic changes and the environment. Diagnosis is based on social and communication difficulties, as well as the presence of restricted and repetitive behaviours. Autism aetiology is complex. However, the social motivation hypothesis proposes that an imbalance in the salience of social over non-social stimuli contributes over time to the autism phenotype. Accordingly, motivational dysfunction in autism is widespread, and human imaging data has identified broad impairments to reward processing. The R451C mutation of the neuroligin-3 gene is one such rare genetic change. Knock-in mice harbouring this mutation (NL3) exhibit a range of autism-related phenotypes, including impaired sociability and social motivation. However, no prior report has directly probed non-social motivation. Here, we explore conflicting results from the progressive ratio (PR) and conditioned place preference tasks of non-social motivation. Initial PR results were inconsistent, suggesting reduced, unaltered, and elevated non-social motivation, respectively. Utilising several experimental designs, we probed a range of confounders likely to influence task performance. Overall, reduced PR responding by NL3s likely arose from a combination of their superior ability to withhold responding during prior training and a short PR training schedule. Meanwhile, increased PR responding by NL3s was attributable to their heightened degree of habitual responding. The NL3 mouse model therefore likely best represents autistic individuals with intact non-social motivation but altered behavioural updating. Finally, we discuss the benefits and limitations of using heterogenous experimental designs to probe behavioural phenotypes and offer some general recommendations for PR
Combining Classical and Probabilistic Independence Reasoning to Verify the Security of Oblivious Algorithms
No full textAbstract
We consider the problem of how to verify the security of probabilistic oblivious algorithms formally and systematically. Unfortunately, prior program logics fail to support a number of complexities that feature in the semantics and invariants needed to verify the security of many practical probabilistic oblivious algorithms. We propose an approach based on reasoning over perfectly oblivious approximations, using a program logic that combines both classical Hoare logic reasoning and probabilistic independence reasoning to support all the needed features. We formalise and prove our new logic sound in Isabelle/HOL and apply our approach to formally verify the security of several challenging case studies beyond the reach of prior methods for proving obliviousness
Carbon sequestering biochar incorporated cementitious composites: Evaluation of hygrothermal, mechanical and durability characteristics
No full textIn the realm of sustainable construction materials, this study delves into the feasibility of utilizing wood-derived biochar as a partial substitute for sand in mortar. Carbon mineralisation potential of mortar increases due to the presence of biochar. Inclusion of biochar leads to improved thermal performance, manifested through reduced thermal conductivity, and increased specific heat capacity. Water vapour resistance factor also benefits from biochar, peaking at a 15 % mixture. However, it is essential to acknowledge that these hygrothermal and carbon sequestration advantages comes at a cost: higher biochar contents lead to reduced strength, increased drying shrinkage and reduced sulphate resistance. The primary focus of this research lies in striking a balance between hygrothermal performance and environmental performance, particularly for indoor building applications. Furthermore, this research underscores the necessity of tailoring biochar-cementitious composite materials to their intended application context, capitalizing on their inherent strengths while mitigating potential weaknesses
How trust networks shape students’ opinions about the proficiency of artificially intelligent assistants
No full textThe rising use of educational tools controlled by artificial intelligence (AI) has provoked a debate about their proficiency. While intrinsic proficiency, especially in tasks such as grading, has been measured and studied extensively, perceived proficiency remains underexplored. Here it is shown through Monte Carlo multi-agent simulations that trust networks among students influence their perceptions of the proficiency of an AI tool. A probabilistic opinion dynamics model is constructed, in which every student's perceptions are described by a probability density function (PDF), which is updated at every time step through independent, personal observations and peer pressure shaped by trust relationships. It is found that students infer correctly the AI tool's proficiency θAI in allies-only networks (i.e. high trust networks). AI-avoiders reach asymptotic learning faster than AI-users, and the asymptotic learning time for AI-users decreases as their number increases. However, asymptotic learning is disrupted even by a single partisan, who is stubbornly incorrect in their belief θp≠θAI, making other students’ beliefs vacillate indefinitely between θp and θAI. In opponents-only (low trust) networks, all students reach asymptotic learning, but only a minority infer θAI correctly. AI-users have a small advantage over AI-avoiders in reaching the right conclusion. The outcomes in allies-only and opponents-only networks depend weakly on network size n. In mixed networks, students may exhibit turbulent nonconvergence and intermittency, or achieve asymptotic learning, depending on the relationships between partisans and AI-users. In smaller mixed networks with n≲10 students, the long-term outcome is affected by whether a partisan teacher is an AI-skeptic (θpAI) or an AI-promoter (θp≥θAI). In larger mixed networks with n≳102, students are more likely to infer θp instead of θAI. The educational implications of the results are discussed briefly in the context of designing robust usage policies for AI tools, with an emphasis on the unintended and inequitable consequences which arise sometimes from counterintuitive network effects
Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions
No full textWe propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework